From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
Ground Truth has 39 facts recorded in Dontopedia across 9 references, with 6 live disagreements.
Mostly:rdf:type(9), used by(7), rdfs:label(4)
rdfs:labelusedByinverseRelationcontainsQuerymembergeneratedByrelation_tosetElementTypedescriptioninstantiatedByOther subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. “X motherOf this subject” — and answer questions the forward facts can't. Grouped by predicate.
requiresRequires(6)ex:accuracy_scoreex:f1_scoreex:f1_scoreex:precision_scoreex:recall_scoreex:sparse_retrieval_enginecalledWithCalled With(3)ex:f1_scoreex:precision_scoreex:recall_scorecomparesCompares(3)ex:f1_scoreex:precision_scoreex:recall_scorecomparesAgainstCompares Against(3)ex:sparse_retrieval_engineex:Validatorex:ValidatorhasColumnHas Column(3)ex:dataset_columnsex:queries_dataset.csvex:queries_dataset_csvextractsColumnExtracts Column(2)ex:outputs extractionex:outputs_extractionrepresentsRepresents(2)ex:true_labelsex:true_labelscontainsVariableContains Variable(1)ex:ground-truth-examplecreatesCreates(1)ex:generate_ground_truthdependsOnDepends on(1)ex:test_iterationgeneratesGenerates(1)ex:generate_ground_truthhasArgumentHas Argument(1)ex:evaluate_accuracy-functionhasVariableHas Variable(1)ex:Validator.__call__producesProduces(1)ex:generate_ground_truthreturnsOnConditionReturns on Condition(1)ex:Validator.__call__The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Cardinality | Num Queries | [2] |
| Shape | (num Queries,) | [2] |
| Assigned by | Generate Ground Truth | [1] |
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
doc:beam/ab86a7b2-f677-45b2-b1d3-d2413153a445ground_truth = generate_ground_truth(num_queries, num_relevant) with Timer() as timer: results = engine.search(test_data) total_duration += timer.duration total_throughput += num_queries…
doc:beam/1ef64215-a22e-4070-b268-e4748745aa75def evaluate_accuracy(tuned_queries, ground_truth): # Evaluate the accuracy of the tuned queries correct = 0 for query in tuned_queries: if query['id'] in ground_truth: correct += 1 return correct / len(t…
doc:beam/575c6f15-a6fa-439f-9d3d-ef28e0854e79best_score = grid_search.best_score_ print(f"Best parameters: {best_params}") print(f"Best cross-validation accuracy: {best_score:.4f}") # Re-fit with best parameters pipeline.set_params(**best_params) pipeline.fit(X_train, y_train) # Fi…
doc:beam/d12b2d61-e885-4664-a34c-5efbe1a9589cinputs = data['input'] outputs = data['output'] # Split the data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(inputs, outputs, test_size=0.2) # Train the pipeline on the training data pipeline.fit(X_t…
doc:beam/7c39567a-d596-4c72-aa0d-d70287a5c1e4# Calculate accuracy for each engine accuracy1 = np.mean(np.argmax(scores1, axis=1) == true_labels) accuracy2 = np.mean(np.argmax(scores2, axis=1) == true_labels) # Update weights based on accuracy new_weights = (ac…
doc:beam/32ec640f-9ed8-491e-bf90-30f5a7ef6971transformed_outputs = pipeline.transform(X_test) # Evaluate the performance accuracy = (transformed_outputs == y_test).mean() print(f'Transformation accuracy: {accuracy:.2%}') ``` ### Explanation 1. **TextPreprocessor**: Cleans and prepr…
doc:beam/611f4162-5cb7-44ec-ba74-e4a6eac30c99[Turn 10605] Assistant: Collecting and structuring a dataset for evaluating the reformulated queries involves several steps. Here's a comprehensive guide to help you create a robust dataset and structure it effectively: ### Step 1: Define …
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